Online Distributed Maritime Event Detection & Forecasting over Big Vessel Tracking Data

Marios Vodas, Konstantina Bereta, Dimitris Kladis, Dimitris Zissis, Emmanouil Ntoulias, Elias Alevizos, Alexander Artikis, David Arnu, Edwin Yaqub, Fabian Temme, Mate Torok, Ralf Klinkenberg - IEEE International Conference on Big Data 2021 - IEEE BigData (online conference) 2021


We present a Maritime Situational Awareness (MSA) framework for detecting and forecasting maritime events (e.g., illegal fishing) over streams of Big maritime Data. The architecture of the MSA framework relies on the following state-of-the-art components: (i) the Maritime Event Detector which uses data-driven distributed techniques deployed on a computer cluster to detect maritime events of interest in an online, real-time fashion, (ii) the Complex Event Forecasting module, which implements state-of-the-art distributed Complex Event Forecasting techniques for maritime data, (iii) the Synopses Data Engine component, that creates synopses of maritime data improving the scalability of the framework and (iv) the streaming extension of a popular data science platform, namely RapidMiner Studio, that integrates all the above, allowing users to graphically design and rapidly implement Big Data analytics pipelines which can be deployed transparently on top of distributed architectures.

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This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 825070.

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